New local difference binary image descriptor and algorithm for rapid and precise vehicle visual localisation
Autonomous vehicle self‐localisation by scene matching under extreme environmental changes has been among the most challenging problems in robotics and computer vision in the last few years. Large dynamic illumination changes during the day and appearance changes between different seasons are the ma...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-08-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2018.5203 |
| _version_ | 1797684768825933824 |
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| author | Ahmed Bibars Mohsen Mahroos |
| author_facet | Ahmed Bibars Mohsen Mahroos |
| author_sort | Ahmed Bibars |
| collection | DOAJ |
| description | Autonomous vehicle self‐localisation by scene matching under extreme environmental changes has been among the most challenging problems in robotics and computer vision in the last few years. Large dynamic illumination changes during the day and appearance changes between different seasons are the major difficulties about this problem, especially when the comparison is made between day‐time and night‐time images for the same scene. This study presents a new extended local difference binary (ELDB) image descriptor that represents a robust appearance invariant extension for the state‐of‐the‐art local difference binary (LDB) image descriptor. This study also introduces a new algorithm for vehicle visual localisation under extreme environmental changes. The new algorithm uses ELDB for image matching, and uses a modified multi‐hypothesis version of the Markov localisation (MHML) filter for self‐localisation. Experimental results show that the proposed modified MHML has reduced computational cost and has resulted in a faster cycle rate. Furthermore, these results show that ELDB has an improved image matching accuracy and requires less processing time compared to the original LDB. The proposed vision‐based vehicle localisation algorithm is shown to be faster and more accurate than other state‐of‐the‐art algorithms. |
| first_indexed | 2024-03-12T00:34:34Z |
| format | Article |
| id | doaj.art-dd6de93957aa44fc9f5455e1b0f8220c |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:34:34Z |
| publishDate | 2019-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-dd6de93957aa44fc9f5455e1b0f8220c2023-09-15T10:01:39ZengWileyIET Computer Vision1751-96321751-96402019-08-0113544345110.1049/iet-cvi.2018.5203New local difference binary image descriptor and algorithm for rapid and precise vehicle visual localisationAhmed Bibars0Mohsen Mahroos1Faculty of Engineering, Cairo UniversityGiza12613EgyptFaculty of Engineering, Cairo UniversityGiza12613EgyptAutonomous vehicle self‐localisation by scene matching under extreme environmental changes has been among the most challenging problems in robotics and computer vision in the last few years. Large dynamic illumination changes during the day and appearance changes between different seasons are the major difficulties about this problem, especially when the comparison is made between day‐time and night‐time images for the same scene. This study presents a new extended local difference binary (ELDB) image descriptor that represents a robust appearance invariant extension for the state‐of‐the‐art local difference binary (LDB) image descriptor. This study also introduces a new algorithm for vehicle visual localisation under extreme environmental changes. The new algorithm uses ELDB for image matching, and uses a modified multi‐hypothesis version of the Markov localisation (MHML) filter for self‐localisation. Experimental results show that the proposed modified MHML has reduced computational cost and has resulted in a faster cycle rate. Furthermore, these results show that ELDB has an improved image matching accuracy and requires less processing time compared to the original LDB. The proposed vision‐based vehicle localisation algorithm is shown to be faster and more accurate than other state‐of‐the‐art algorithms.https://doi.org/10.1049/iet-cvi.2018.5203new local difference binary image descriptorrapid vehicle visual localisationprecise vehicle visual localisationautonomous vehicle self-localisationscene matchingcomputer vision |
| spellingShingle | Ahmed Bibars Mohsen Mahroos New local difference binary image descriptor and algorithm for rapid and precise vehicle visual localisation IET Computer Vision new local difference binary image descriptor rapid vehicle visual localisation precise vehicle visual localisation autonomous vehicle self-localisation scene matching computer vision |
| title | New local difference binary image descriptor and algorithm for rapid and precise vehicle visual localisation |
| title_full | New local difference binary image descriptor and algorithm for rapid and precise vehicle visual localisation |
| title_fullStr | New local difference binary image descriptor and algorithm for rapid and precise vehicle visual localisation |
| title_full_unstemmed | New local difference binary image descriptor and algorithm for rapid and precise vehicle visual localisation |
| title_short | New local difference binary image descriptor and algorithm for rapid and precise vehicle visual localisation |
| title_sort | new local difference binary image descriptor and algorithm for rapid and precise vehicle visual localisation |
| topic | new local difference binary image descriptor rapid vehicle visual localisation precise vehicle visual localisation autonomous vehicle self-localisation scene matching computer vision |
| url | https://doi.org/10.1049/iet-cvi.2018.5203 |
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